186 lines
7.0 KiB
Python
186 lines
7.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import errno
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import os
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import pickle
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import six
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import paddle
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from ppocr.utils.logging import get_logger
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__all__ = ['load_model']
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def _mkdir_if_not_exist(path, logger):
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"""
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mkdir if not exists, ignore the exception when multiprocess mkdir together
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"""
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if not os.path.exists(path):
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try:
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os.makedirs(path)
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except OSError as e:
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if e.errno == errno.EEXIST and os.path.isdir(path):
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logger.warning(
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'be happy if some process has already created {}'.format(
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path))
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else:
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raise OSError('Failed to mkdir {}'.format(path))
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def load_model(config, model, optimizer=None, model_type='det'):
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"""
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load model from checkpoint or pretrained_model
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"""
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logger = get_logger()
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global_config = config['Global']
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checkpoints = global_config.get('checkpoints')
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pretrained_model = global_config.get('pretrained_model')
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best_model_dict = {}
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if model_type == 'vqa':
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checkpoints = config['Architecture']['Backbone']['checkpoints']
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# load vqa method metric
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if checkpoints:
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if os.path.exists(os.path.join(checkpoints, 'metric.states')):
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with open(os.path.join(checkpoints, 'metric.states'),
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'rb') as f:
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states_dict = pickle.load(f) if six.PY2 else pickle.load(
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f, encoding='latin1')
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best_model_dict = states_dict.get('best_model_dict', {})
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if 'epoch' in states_dict:
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best_model_dict['start_epoch'] = states_dict['epoch'] + 1
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logger.info("resume from {}".format(checkpoints))
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if optimizer is not None:
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if checkpoints[-1] in ['/', '\\']:
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checkpoints = checkpoints[:-1]
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if os.path.exists(checkpoints + '.pdopt'):
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optim_dict = paddle.load(checkpoints + '.pdopt')
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optimizer.set_state_dict(optim_dict)
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else:
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logger.warning(
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"{}.pdopt is not exists, params of optimizer is not loaded".
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format(checkpoints))
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return best_model_dict
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if checkpoints:
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if checkpoints.endswith('.pdparams'):
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checkpoints = checkpoints.replace('.pdparams', '')
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assert os.path.exists(checkpoints + ".pdparams"), \
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"The {}.pdparams does not exists!".format(checkpoints)
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# load params from trained model
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params = paddle.load(checkpoints + '.pdparams')
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state_dict = model.state_dict()
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new_state_dict = {}
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for key, value in state_dict.items():
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if key not in params:
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logger.warning("{} not in loaded params {} !".format(
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key, params.keys()))
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continue
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pre_value = params[key]
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if list(value.shape) == list(pre_value.shape):
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new_state_dict[key] = pre_value
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else:
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logger.warning(
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"The shape of model params {} {} not matched with loaded params shape {} !".
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format(key, value.shape, pre_value.shape))
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model.set_state_dict(new_state_dict)
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if optimizer is not None:
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if os.path.exists(checkpoints + '.pdopt'):
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optim_dict = paddle.load(checkpoints + '.pdopt')
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optimizer.set_state_dict(optim_dict)
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else:
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logger.warning(
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"{}.pdopt is not exists, params of optimizer is not loaded".
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format(checkpoints))
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if os.path.exists(checkpoints + '.states'):
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with open(checkpoints + '.states', 'rb') as f:
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states_dict = pickle.load(f) if six.PY2 else pickle.load(
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f, encoding='latin1')
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best_model_dict = states_dict.get('best_model_dict', {})
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if 'epoch' in states_dict:
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best_model_dict['start_epoch'] = states_dict['epoch'] + 1
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logger.info("resume from {}".format(checkpoints))
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elif pretrained_model:
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load_pretrained_params(model, pretrained_model)
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else:
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logger.info('train from scratch')
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return best_model_dict
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def load_pretrained_params(model, path):
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logger = get_logger()
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if path.endswith('.pdparams'):
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path = path.replace('.pdparams', '')
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assert os.path.exists(path + ".pdparams"), \
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"The {}.pdparams does not exists!".format(path)
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params = paddle.load(path + '.pdparams')
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state_dict = model.state_dict()
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new_state_dict = {}
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for k1 in params.keys():
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if k1 not in state_dict.keys():
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logger.warning("The pretrained params {} not in model".format(k1))
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else:
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if list(state_dict[k1].shape) == list(params[k1].shape):
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new_state_dict[k1] = params[k1]
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else:
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logger.warning(
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"The shape of model params {} {} not matched with loaded params {} {} !".
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format(k1, state_dict[k1].shape, k1, params[k1].shape))
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model.set_state_dict(new_state_dict)
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logger.info("load pretrain successful from {}".format(path))
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return model
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def save_model(model,
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optimizer,
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model_path,
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logger,
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config,
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is_best=False,
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prefix='ppocr',
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**kwargs):
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"""
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save model to the target path
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"""
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_mkdir_if_not_exist(model_path, logger)
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model_prefix = os.path.join(model_path, prefix)
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paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
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if config['Architecture']["model_type"] != 'vqa':
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paddle.save(model.state_dict(), model_prefix + '.pdparams')
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metric_prefix = model_prefix
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else:
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if config['Global']['distributed']:
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model._layers.backbone.model.save_pretrained(model_prefix)
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else:
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model.backbone.model.save_pretrained(model_prefix)
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metric_prefix = os.path.join(model_prefix, 'metric')
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# save metric and config
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with open(metric_prefix + '.states', 'wb') as f:
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pickle.dump(kwargs, f, protocol=2)
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if is_best:
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logger.info('save best model is to {}'.format(model_prefix))
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else:
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logger.info("save model in {}".format(model_prefix))
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